AWS SageMaker Ground Truth Job Finished Successfully Without Assigning Tasks to Requested Number of Human Workers: A Step-by-Step Guide to Troubleshooting

If you’re reading this article, chances are you’ve encountered a frustrating issue with your AWS SageMaker Ground Truth job. You’ve set up your job, requested a certain number of human workers, and waited patiently for the results. But to your surprise, the job finishes successfully without assigning tasks to the requested number of human workers. Don’t worry, we’ve all been there! In this article, we’ll walk you through a step-by-step guide to troubleshoot and resolve this issue.

Understanding the Issue

Before we dive into the troubleshooting process, let’s understand what might be causing this issue. There are several reasons why your AWS SageMaker Ground Truth job might not be assigning tasks to the requested number of human workers, including:

• Invalid or incomplete worker requirements
• Inadequate worker availability
• Insufficient task pricing
• Job configuration issues
• AWS service limits or throttling

Step 1: Review Worker Requirements

The first step in troubleshooting is to review your worker requirements. Ensure that you’ve specified the correct requirements for your workers, including:

• Worker type (e.g., labeler, transcriber, etc.)
• Worker skills (e.g., language proficiency, domain expertise, etc.)
• Worker demographics (e.g., location, age range, etc.)

Check your worker requirements against the AWS SageMaker Ground Truth documentation to ensure you’ve specified them correctly. You can also try adjusting your worker requirements to see if that resolves the issue.

Worker Requirements Example

{
  "WorkerRequirements": {
    "Type": "labeler",
    "Skills": ["English language proficiency", "Domain expertise in healthcare"],
    "Demographics": {
      "Country": "US",
      "AgeRange": "25-45"
    }
  }
}

Step 2: Check Worker Availability

Another potential reason for the issue is inadequate worker availability. You can check the worker availability for your specific requirements using the AWS SageMaker Ground Truth console or API.

Follow these steps to check worker availability using the console:

1. Log in to the AWS Management Console and navigate to the AWS SageMaker Ground Truth dashboard.
2. Click on the “Workers” tab and select “Worker availability” from the dropdown menu.
3. Enter your worker requirements and click “Get availability” to view the available workers.

Worker Availability API Example

import boto3

ground_truth = boto3.client('sagemaker')

response = ground_truth.get_worker_availability(
    WorkerRequirements={
        'Type': 'labeler',
        'Skills': ['English language proficiency', 'Domain expertise in healthcare'],
        'Demographics': {
            'Country': 'US',
            'AgeRange': '25-45'
        }
    }
)

print(response['AvailableWorkers'])

Step 3: Inspect Task Pricing

Insufficient task pricing can also cause issues with worker assignment. Ensure that your task pricing is competitive and attractive to workers. You can check your task pricing using the AWS SageMaker Ground Truth console or API.

Follow these steps to check task pricing using the console:

1. Log in to the AWS Management Console and navigate to the AWS SageMaker Ground Truth dashboard.
2. Click on the “Tasks” tab and select “Task pricing” from the dropdown menu.
3. Enter your task requirements and click “Get pricing” to view the recommended pricing.

Task Pricing API Example

import boto3

ground_truth = boto3.client('sagemaker')

response = ground_truth.get_task_pricing(
    TaskRequirements={
        'Type': 'labeling-task',
        'InputData': {
            'S3Uri': 's3://my-bucket/input-data.csv'
        },
        'OutputData': {
            'S3Uri': 's3://my-bucket/output-data.csv'
        }
    }
)

print(response['Pricing'])

Step 4: Review Job Configuration

Job configuration issues can also cause problems with worker assignment. Ensure that your job configuration is correct and complete, including:

• Job type (e.g., labeling, transcription, etc.)
• Input data (e.g., S3 URI, data format, etc.)
• Output data (e.g., S3 URI, data format, etc.)
• Task requirements (e.g., worker requirements, task pricing, etc.)

Check your job configuration against the AWS SageMaker Ground Truth documentation to ensure you’ve specified it correctly.

Job Configuration Example

{
  "JobType": "labeling-job",
  "InputData": {
    "S3Uri": "s3://my-bucket/input-data.csv",
    "DataFormat": "csv"
  },
  "OutputData": {
    "S3Uri": "s3://my-bucket/output-data.csv",
    "DataFormat": "csv"
  },
  "TaskRequirements": {
    "Type": "labeler",
    "Skills": ["English language proficiency", "Domain expertise in healthcare"],
    "Demographics": {
      "Country": "US",
      "AgeRange": "25-45"
    },
    "Pricing": {
      "PriceInCents": 50
    }
  }
}

Step 5: Check AWS Service Limits and Throttling

AWS service limits and throttling can also cause issues with worker assignment. Ensure that you’re not exceeding the maximum number of concurrent jobs or tasks for your account.

You can check your AWS service limits using the AWS Management Console or API.

AWS Service Limits API Example

import boto3

cloudwatch = boto3.client('cloudwatch')

response = cloudwatch.describe_alarm_history(
    AlarmName='SageMakerGroundTruth-Job-Count',
    HistoryItemType='ConfigurationUpdate'
)

print(response['AlarmHistoryItems'])

Conclusion

In this article, we’ve walked you through a step-by-step guide to troubleshooting the “AWS SageMaker Ground Truth job finished successfully without assigning tasks to requested number of human workers” issue. By following these steps, you should be able to identify and resolve the root cause of the issue.

Remember to review your worker requirements, check worker availability, inspect task pricing, review job configuration, and check AWS service limits and throttling. With these troubleshooting steps, you’ll be able to get your AWS SageMaker Ground Truth job up and running smoothly.

Troubleshooting Step	Potential Cause	Solution
Review Worker Requirements	Invalid or incomplete worker requirements	Check AWS SageMaker Ground Truth documentation for correct worker requirements
Check Worker Availability	Inadequate worker availability	Adjust worker requirements or increase task pricing
Inspect Task Pricing	Insufficient task pricing	Increase task pricing or adjust worker requirements
Review Job Configuration	Job configuration issues	Check AWS SageMaker Ground Truth documentation for correct job configuration
Check AWS Service Limits and Throttling	AWS service limits or throttling	Check AWS service limits and adjust job configuration accordingly

We hope this article has been helpful in resolving the “AWS SageMaker Ground Truth job finished successfully without assigning tasks to requested number of human workers” issue. If you have any further questions or concerns, please don’t hesitate to reach out.

Frequently Asked Question

Stuck with your AWS SageMaker Ground Truth job? Don’t worry, we’ve got you covered! Here are some frequently asked questions and answers to help you troubleshoot common issues.